Information retrieval systems are capable of accessing enormous volumes of information. As a result, locating information of interest to users presents challenges. One such challenge is identifying information that may be of interest to users so that information may be presented to them without overwhelming users with irrelevant information. Even in environments, such as online search, where the user provides an explicit indication (e.g., a search query) of what information the user may be interested in, such an indication may not be sufficient to accurately identify the content which is appropriate to present to the user from among all the content that may be available to be presented to the user.
Conventional approaches to identifying information of interest to a user often shift the burden of finding such information to the user. For example, conventional approaches to search may involve presenting all potentially relevant results to a user in response to the user's search query. Subsequently, the user has to manually explore and/or rank these results in order to find the information of greatest interest to him. When the number of potentially relevant results is large, which is often the case, the user may be overwhelmed and may fail to locate the information he is seeking.
One technique for addressing this problem is to integrate a user's preferences into the process of identifying information of interest to the user. By presenting information to the user in accordance with his preferences, the user may be helped to find the information he is seeking. However, conventional approaches to specifying user preferences severely limit the ways in which user preferences may be specified, thereby limiting the utility of such approaches.
Consider, for example, a data exploration model adopted by many search services and illustrated in FIG. 1. Query interface 12 is used to collect query predicates in the form of keywords and/or attribute values (e.g., “used Toyota” with price in the range [$2000-$5000]). Query results are then sorted (14) on the values of one or more attributes (e.g., order by Price then by Rating) in a major sort/minor sort fashion. The user then scans (16) through the sorted query answers to locate items of interest, refines query predicates, and repeats the exploration cycle (18). This “Query, Sort, then Scan” model limits the flexibility of preference specification and imposes rigid information retrieval schemes, as highlighted in the following example.